Abstract
This paper presents a closed edge detection method based on a level lines selection approach. The proposed method is based on an unsupervised probabilistic scheme using an a contrario method. A level line is considered meaningful if its contrast and length is unlikely to be due to chance. Besides being unsupervised, this method exploits a tree structure. The first step of the proposed approach is to reduce the meaningful level lines set using this hierarchical structure. Compared with a previous method using the same principle, our method achieve a 67% reduction rate of irrelevant levels lines. The second step of the proposed approach illustrates the high flexibility of using closed edge boundaries such as levels lines. Using a rather simple curvature analysis, the proposed method detects anatomical structures boundaries from CT scan images.
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Hurtut, T., Cheriet, F. (2007). Automatic Closed Edge Detection Using Level Lines Selection. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2007. Lecture Notes in Computer Science, vol 4633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74260-9_17
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DOI: https://doi.org/10.1007/978-3-540-74260-9_17
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